SoroushVahidi/scidocs huggingface dataset:
SUSAN—A New Approach to Low Level Image Processing
(1997)

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.
An accident prediction approach based on XGBoost
(2017)

As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.
Generating spiking time series with Generative Adversarial Networks : an application on banking transactions by Luca Simonetto 11413522 September 2018
(2018)

The task of data generation using Generative Models has recently gained more and more attention from the scientific community, as the number of applications in which these models work surprisingly well is constantly increasing. Some examples are image and video generation, speech synthesis and style transfer, pose guided image generation, cross-domain transfer and super resolution. Contrarily to such tasks generating data coming from the banking domain poses a different challenge, due to its atypical structure when compared with traditional data and its limited availability due to privacy restrictions. In this work, we analyze the feasibility of generating spiking time series patterns appearing in the banking environment using Generative Adversarial Networks. We develop a novel end-to-end framework for training, testing and comparing different generative models using both quantitative and qualitative metrics. Finally, we propose a novel approach that combines Variational Autoencoders with Generative Adversarial Networks in order to learn a loss function for datasets in which good similarity metrics are difficult to define.
Emotional stability, core self-evaluations, and job outcomes: A review of the evidence and an agenda for future research
(2004)

In this article we present a review of research on core self-evaluations, a broad personality trait indicated by 4 more narrow traits: self-esteem, generalized self-efficacy, locus of control, and emotional stability. We review evidence suggesting that the 4 core traits are highly related, load on a single unitary factor, and have dubious incremental validity controlling for their common core. We more generally investigate the construct validity of core self-evaluations. We also report on the development and validation of the first direct measure of the concept, the Core Self-Evaluations Scale (CSES). Cross-cultural evidence on the CSES is provided. We conclude by offering an agenda for future research, discussing areas where future core self-evaluations research is most needed.
Analysis of digital bang-bang clock and data recovery for multi-gigabit/s serial transceivers
(2009)

A Harmonic Balance method for analyzing digital bang-bang clock and data recovery (CDR) is proposed in this paper. The jitter tolerance performance of the CDR is predicted by a function with variables that can be easily correlated to design parameters. A 6.25Gb/s serial transceiver was fabricated in 90nm CMOS technology. Measurements show that the jitter tolerance performance can be accurately predicted by the proposed method.
Compact Circularly-Polarized Patch Antenna Loaded With Metamaterial Structures
(2011)

A metamaterial-inspired low-profile patch antenna is proposed and studied for circularly-polarized (CP) radiation. The present antenna, which has a single-fed configuration, is loaded with the composite right/left-handed (CRLH) mushroom-like structures and a reactive impedance surface (RIS) for miniaturization purpose. The CP radiation is realized by exciting two orthogonally-polarized modes simultaneously which are located in the left-handed (LH) region. The detailed antenna radiation characteristics are examined and illustrated with both simulated and experimental results. The CP performance can be controlled in several different ways. This antenna exhibits an overall size of 0.177λ0 × 0.181λ0 × 0.025λ0 at 2.58 GHz and a radiation efficiency around 72%. Finally, based on the proposed CP patch antenna, a compact dual-band dual linearly-polarized patch antenna has also been designed and fabricated. Promising experimental results are observed.
Low Profile Fully Planar Folded Dipole Antenna on a High Impedance Surface
(2012)

A fully planar antenna design incorporating a high impedance surface (HIS) is presented. The HIS is composed by a periodic array of subwavelength dogbone-shaped conductors printed on top of a thin dielectric substrate and backed by a metallic ground plane. First, the characteristics of a dipole over PEC or PMC layers, a dielectric slab, and the HIS are compared and studied in detail, highlighting the advantages provided by the use of the HIS. Then, the design of a low profile folded dipole antenna working at 5.5 GHz on top of the HIS is described. The surface provides close to 6% antenna impedance bandwidth and increased gain up to 7 dBi, while shielding the lower half space from radiation. The antenna structure comprises three metal layers without any vias between them, and its overall thickness is 0.059λ0. The dipole is fed by a balanced twin lead line through a balun transformer integrated in the same antenna layer. A prototype has been built and measurements confirming simulation results are provided.
A Compact, Low-Profile Metasurface-Enabled Antenna for Wearable Medical Body-Area Network Devices
(2014)

We propose a compact conformal wearable antenna that operates in the 2.36-2.4 GHz medical body-area network band. The antenna is enabled by placing a highly truncated metasurface, consisting of only a two by two array of I-shaped elements, underneath a planar monopole. In contrast to previously reported artificial magnetic conducting ground plane backed antenna designs, here the metasurface acts not only as a ground plane for isolation, but also as the main radiator. An antenna prototype was fabricated and tested, showing a strong agreement between simulation and measurement. Comparing to previously proposed wearable antennas, the demonstrated antenna has a compact form factor of 0.5 λ0 ×0.3 λ0 ×0.028 λ0, all while achieving a 5.5% impedance bandwidth, a gain of 6.2 dBi, and a front-to-back ratio higher than 23 dB. Further numerical and experimental investigations reveal that the performance of the antenna is extraordinarily robust to both structural deformation and human body loading, far superior to both planar monopoles and microstrip patch antennas. Additionally, the introduced metal backed metasurface enables a 95.3% reduction in the specific absorption rate, making such an antenna a prime candidate for incorporation into various wearable devices.
Mining Contextual Preference Rules for Building User Profiles
(2012)

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying some important properties (soundness and conciseness). We evaluate the efficacy of the proposed method in a series of experiments executed on a real-world database of user preferences about movies.
Wearable driver drowsiness detection using electrooculography signal
(2016)

Every year, more than 100,000 automobile crashes are caused by driver drowsiness. Various technologies have been developed to address this issue, including vehicle-based measurements, behavior change detection, and physiological signal analysis. Both vehicle-based measurements and behavior change detection require bulky components. They also identify the driver's drowsiness too late for effective accident prevention. The physiological signal changes in an early stage and can be used to detect the on-set of driver drowsiness. In this paper, the development of a wearable drowsiness detection system is introduced. This system measures the electrooculography (EOG) signal; transmits the signal to a smartphone wirelessly; and could alarm the driver based on a prediction algorithm that can estimate 0.5-second-ahead EOG signal behavior. This system is compact, comfortable, and cost effective. The 0.5-second-ahead estimation capability provides the critical time for a driver to correct the behavior, and ultimately saves lives.
Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation
(2017)

The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods.
Design, Fabrication, and Characterization of a 3-D CMOS Fluxgate Magnetometer
(2011)

A dual-core 3-D microfluxgate magnetometer fabricated by a simple and inexpensive fabrication process is described in this paper. The microfluxgate is able to operate along a nearly linear V-B relationship at the second harmonic frequency and features good characteristics of high sensitivity and low noise response. These characteristic results indicate a field-to-voltage transfer coefficient of 11 V/T measured at the second harmonic frequency, power consumption of 67.3 mW, and a field noise response less than 12 nT/√ Hz at 1 Hz. In brief, our proposed device not only enhances responsivity capability and linear V-B characteristics, but also is CMOS process compatible, which is considered both function-efficient and cost-effective.
Performance Analysis of Big Data Gathering in Wireless Sensor Network Using an EM Based Clustering Scheme
(2015)

Big-data is a popular term in the field of information and communication technology. Wireless Sensor Networks (WSN) is one of the eminent contributors of big data. WSN contains numerous sensor nodes that cooperatively monitor an environment. Each network consists of sensor node, memory, and communication device. Data generated by single sensor node is small but data generated by distributed sensor network is significantly large and it is termed as big-data. The critical issue in WSN is energy consumption and data gathering. This paper mainly focus on Expectation Maximization (EM) based clustering scheme implementation and the performance analysis of WSN using single mobile sink to eight mobile sink. From the analysis we also derived a relationship between the number of mobile sinks required for a particular network with a given number of sensor nodes. Experimental results show that the number of mobile sinks is also an important parameter to efficiently gather information in WSN.
All-dielectric subwavelength metasurface focusing lens.
(2014)

We have proposed, designed, manufactured and tested low loss dielectric micro-lenses for infrared (IR) radiation based on a dielectric metamaterial layer. This metamaterial layer was created by patterning a dielectric surface and etching to sub-micron depths. For a proof-of-concept lens demonstration, we have chosen a fine patterned array of nano-pillars with variable diameters. Gradient index (GRIN) properties were achieved by engineering the nano-pattern characteristics across the lens, so that the effective optical density of the dielectric metamaterial layer peaks around the lens center, and gradually drops at the lens periphery. A set of lens designs with reduced reflection and tailorable phase gradients have been developed and tested, demonstrating focal distances of a few hundred microns, beam area contraction ratio up to three, and insertion losses as low as 11%.
Ontology-Based Information Extraction for Knowledge Enrichment and Validation
(2016)

Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.
SecMANO: Towards Network Functions Virtualization (NFV) Based Security MANagement and Orchestration
(2016)

Network Functions Virtualization (NFV) has recently emerged as one of the major technological driving forces that significantly accelerate the evolution of today's computer and communication networks. Despite the advantages of NFV, e.g., saving investment cost, optimizing resource consumption, improving operational efficiency, simplifying network service lifecycle management, lots of novel security threats and vulnerabilities will be introduced, thereby impeding its further development and deployment in practice. In this paper, we briefly report our threat analysis in the context of NFV, and identify the corresponding security requirements. The purpose is to establish a comprehensive threat taxonomy and provide a guideline to develop effective security countermeasures. Furthermore, a conceptual design framework for NFV based security management and service orchestration is presented, with an objective to dynamically and adaptively deploy and manage security functions on the demands of users and customers. A use case about NFV based access control is also developed, illustrating the feasibility and advantages of implementing NFV based security management and orchestration.
Information security management needs more holistic approach: A literature review
(2016)

Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers’ attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management’s roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT nformation architecture infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area. © 2015 Elsevier Ltd. All rights reserved.
Active Learning with Feedback on Both Features and Instances
(2006)

We extend the traditional active learning framework to include feedback on features in addition to labeling instances, and we execute a careful study of the effects of feature selection and human feedback on features in the setting of text categorization. Our experiments on a variety of categorization tasks indicate that there is significant potential in improving classifier performance by feature re-weighting, beyond that achieved via membership queries alone (traditional active learning) if we have access to an oracle that can point to the important (most predictive) features. Our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion of the most relevant features (over 50% in our experiments). We find that on average, labeling a feature takes much less time than labeling a document. We devise an algorithm that interleaves labeling features and documents which significantly accelerates standard active learning in our simulation experiments. Feature feedback can complement traditional active learning in applications such as news filtering, e-mail classification, and personalization, where the human teacher can have significant knowledge on the relevance of features.
Fast image stitching and editing for panorama painting on mobile phones
(2010)

We propose a fast image stitching and editing approach for panorama painting and have implemented it on a mobile phone. A seam search using dynamic programming finds a good boundary along which to merge the source images, and a transition smoothing process using instant image cloning removes merging artifacts, yielding highquality panoramic images. A sequential image stitching procedure requires only keeping the current source image in memory during image stitching, which saves memory and is more suitable to mobile phone implementations.
Studying Evolution with Self-Replicating Computer Programs
(2007)

A critical discussion is presented on the use of self-replicating program systems as tools for the formulation of generalised theories of evolution. Results generated by such systems must be treated with caution, but, if used properly, they can ooer us unprecedented opportunities for empirical , comparative studies. A new system called Cosmos is introduced, which is based upon Ray's Tierra 15]. The major diierence between Cosmos and previous systems is that individual self-replicating programs in Cosmos are modelled (in a very simpliied fashion) on cellular organisms. Previous systems have generally used simpler self-replicators. The hope is that Cosmos may be better able to address questions concerning the sudden emergence of complex multicellular biological organisms during the Cambrian explosion. Results of initial exploratory runs are presented, which are somewhat diierent to those of similar runs on Tierra. These diierences were expected , and indicate the sensitivity of such systems to the precise details of the language in which the self-replicating programs are written. With the strengths and weaknesses of the methodology in mind, some directions for future research with Cosmos are discussed. Within the last decade, computers have become powerful and aaordable enough to enable a number of research groups to study the evolution of life in a new way. Rather than following the traditional approach of trying to capture properties of whole populations in mathematical models, the new approach models a large number of individual self-replicating entities which are competing against each other for resources required for replication. This is achieved by creating a computer which can run a large number of self-replicating programs in parallel 1. 1 In practice, a virtual computer is created (i.e. implemented in software), with parallelism simulated by time-slicing between Tom Ray pioneered this approach with his Tierra system 15, 16]. Since then, a number of other systems have also been developed, including Avida, developed by Chris Adami and Titus Brown 2], Computer Zoo, written by Jakob Skipper 18], and John Koza's system of self-replicating LISP-like programs 8]. Using such a methodology to study evolutionary systems is attractive for a number of reasons. For example, as the self-replicators are being modelled individually rather than as populations, the simulation respects the fact that a gene does not work in isolation. Rather, it is part of a large ensemble of genes which must all work together with some degree of cooperation in order for the individual organism …